A dasymetric map seeks to display statistical
surface data by exhaustively partitioning space into zones where the
zone boundaries reflect the underlying statistical surface variation.
The process of dasymetric mapping is the transformation of
data from a set of arbitrary source zones to a dasymetric map via the
overlay of the source zones with an ancillary data set. In practice,
dasymetric mapping is often considered a particular type of areal
interpolation technique where source zone data are excluded from
certain classes in a categorical ancillary data set. Dasymetric
mapping is applicable to a wide variety of tasks where the user
seeks to refine spatially aggregated data, for example in estimating
local population characteristics in areas where only coarser,
regional resolution census data are available.

This research addresses the design, implementation,
validation, and application of a new ‘intelligent’ dasymetric mapping
(IDM) technique that supports a variety of methods for characterizing the
relationship between the ancillary data and underlying statistical
surface. The technique is referred to as intelligent because an analyst
may establish this relationship subjectively using their own domain
knowledge, extract this relationship from the data using a novel
empirical sampling technique, or combine the subjective and
empirically-based methods.